Bøger i FT Press Analytics serien

In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for students. Using predictive analytics techniques, students can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. Delen's holistic approach covers all this, and more:Data mining processes, methods, and techniquesThe role and management of dataPredictive analytics tools and metricsTechniques for text and web mining, and for sentiment analysisIntegration with cutting-edge Big Data approachesThroughout, Delen promotes understanding by presenting numerous conceptual illustrations, motivational success stories, failed projects that teach important lessons, and simple, hands-on tutorials that set this guide apart from competitors.

Business leaders today are seeking meaningful patterns in their people data that will help them gain a competitive edge. The answer is in the growing discipline of workforce analytics. Much attention has already been paid to the technical requirements for implementing workforce analytics, now, there's a book that covers the organisational development and change management issues that will make or break your success. Drawing on insights from dozens of experts in workforce analytics as well as their own cutting edge experience within IBM, the authors walk step-by-step through setting up and then embedding workforce analytics capabilities. With candid case studies and clear advice from those who have already faced and overcome the challenges associated with workforce analytics, you'll learn how to: Begin with a vision, not data analysis Pick your projects wisely, so you can "earn your keep" with visible, valuable successes Build a team with the right skills to deliver the insights your organisation needs Identify the right stakeholders and sponsors to ensure success Choose the best technology for your analytics needs Handle some of the sensitivities around using employee data in analytics projects Run a successful workforce analytics function for the long term Use storytelling techniques to ensure you can influence organisational decisions or initiatives with the results of your workforce analytics projects Analytics are rapidly becoming pervasive in functions ranging from Finance to Marketing. Now, discover how HR can gain just as much value, by informing every key decision with the best possible insight.

Profit from analytics throughout the entire customer experience and lifecycle Make the most of all the fast-changing data sources now available to you For all ecommerce executives, strategists, entrepreneurs, marketers, analysts, and data scientists Ecommerce Analytics is a complete single-source guide to analytics for your ecommerce business. It brings together all the knowledge and skills you need to solve your unique problems, and transform your data into better decisions and customer experiences. Judah Phillips shows how to use analysis to improve ecommerce marketing and advertising, understand customer behaviour, increase conversion rates, strengthen loyalty, optimise merchandising and product mix, streamline transactions, optimise product mix, and accurately attribute sales. Drawing on extensive experience leading large-scale analytics programs, he also offers expert guidance on building successful analytical teams; surfacing high-value insights via dashboards and visualisation; and managing data governance, security, and privacy. Here are the answers you need to make the most of analytics in ecommerce: throughout your organisation, across your entire customer lifecycle.

In Marketing Data Science, a top faculty member of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on his predictive analytics program at Northwestern, Miller covers segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where his widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web - and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R.